OpenRaiser/Intern-Atlas huggingface dataset:
A Logic-based Tractable Approximation of Probability
Venue: arXiv cs.LO 2022
Year: 2022

We provide a logical framework in which a resource-bounded agent can be seen to perform approximations of probabilistic reasoning. Our main results read as follows. First we identify the conditions under which propositional probability functions can be approximated by a hierarchy of depth-bounded Belief functions. Second we show that under rather palatable restrictions, our approximations of probability lead to uncertain reasoning which, under the usual assumptions in the field, qualifies as tractable.
Post Approvals in Online Communities
Venue: arXiv cs.SI 2022
Year: 2022

In many online communities, community leaders (i.e., moderators and administrators) can proactively filter undesired content by requiring posts to be approved before publication. But although many communities adopt post approvals, there has been little research on its impact on community behavior. Through a longitudinal analysis of 233,402 Facebook Groups, we examined 1) the factors that led to a community adopting post approvals and 2) how the setting shaped subsequent user activity and moderation in the group. We find that communities that adopted post approvals tended to do so following sudden increases in user activity (e.g., comments) and moderation (e.g., reported posts). This adoption of post approvals led to fewer but higher-quality posts. Though fewer posts were shared after adoption, not only did community members write more comments, use more reactions, and spend more time on the posts that were shared, they also reported these posts less. Further, post approvals did not significantly increase the average time leaders spent in the group, though groups that enabled the setting tended to appoint more leaders. Last, the impact of post approvals varied with both group size and how the setting was used, e.g.,, group size mediates whether leaders spent more or less time in the group following the adoption of the setting. Our findings suggest ways that proactive content moderation may be improved to better support online communities.
Decentralized Digital Currency System using Merkle Hash Trees
Venue: arXiv cs.CY 2022
Year: 2022

In India, post the demonetization exercise in 2016, digital payments have become extremely popular. Among them, the volume of transactions using Paytm wallets and UPI (Unified Payment Interface) have grown manifold. The lockdowns due to COVID-19 Pandemic have furthered this trend. Side by side, crypto-currencies such as bitcoin are also gaining traction. Many countries are considering issuing a Digital Currency via their Central Banks. In this paper, we propose a novel Decentralized Digital Currency System (DDCS) that makes use of Merkle Hash-Trees as Authenticated Data Structures. DDCS uses a Ledger-less, distributed, peer-to-peer architecture. We name the proposed currency $δ$-Money. $δ$-Money is intended as a replacement for physical currency and has in-built security features that rival crypto-currencies. Transactions using $δ$-Money happen in a disintermediated manner but with post-facto reconciliation. In place of Central Bank-issued Digital Currency (CBDC), we envisage a scenario where multiple Payment Banks issue digital currencies that have stable valuations without being subject to either volatility or perennial devaluation.
Calibration of the 5G-LENA System Level Simulator in 3GPP reference scenarios
Venue: arXiv cs.NI 2022
Year: 2022

Due to the rapid technology evolution and standardization activity in the mobile communication networks, there is the need for the research community to be able to develop, test and evaluate new and/or already xisting solutions before industrial or real-network implementation. As such, it is essential to have an open-source tool that provides an alternative solution to that of industrial proprietary simulators that are not available for public usage. ns-3 5G-LENA simulator is an end-to-end open-source NR system-level simulator that allows extensive research to be performed. However, it is of great importance to guarantee that the results obtained using the simulator can be comparable to that of industrial simulators and real networks. For this reason, calibrating the simulator based on 3GPP defined specifications is crucial. Based on the above, in this paper we calibrate the ns-3 5G-LENA simulator according to the 3GPP reference results for NR-based outdoor deployments. Moreover, we explore the REM feature provided by the simulator, to ease the calibration process and understand better the radio environment. Results show the resemblance of the simulator performance to that of simulators used as references by 3GPP.
Prompt Distribution Learning
Venue: arXiv cs.CV 2022
Year: 2022

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts.
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
Venue: arXiv cs.AI 2022
Year: 2022

The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort. This paper proposes a sensor frame processing rate (FPR) estimation model, Zhuyi, that quantifies the minimum safe FPR continuously in a driving scenario. Zhuyi can be employed post-deployment as an online safety check and to prioritize work. Experiments conducted using a multi-camera state-of-the-art industry AV system show that Zhuyi's estimated FPRs are conservative, yet the system can maintain safety by processing only 36% or fewer frames compared to a default 30-FPR system in the tested scenarios.
Robot navigation from human demonstration: learning control behaviors with environment feature maps
Venue: arXiv cs.RO 2022
Year: 2022

When working alongside human collaborators in dynamic and unstructured environments, such as disaster recovery or military operation, fast field adaptation is necessary for an unmanned ground vehicle (UGV) to perform its duties or learn novel tasks. In these scenarios, personnel and equipment are constrained, making training with minimal human supervision a desirable learning attribute. We address the problem of making UGVs more reliable and adaptable teammates with a novel framework that uses visual perception and inverse optimal control to learn traversal costs for environment features. Through extensive evaluation in a real-world environment, we show that our framework requires few human demonstrated trajectory exemplars to learn feature costs that reliably encode several different traversal behaviors. Additionally, we present an on-line version of the framework that allows a human teammate to intervene during live operation to correct deteriorated behavior or to adapt behavior to dynamic changes in complex and unstructured environments.
DADApy: Distance-based Analysis of DAta-manifolds in Python
Venue: arXiv cs.LG 2022
Year: 2022

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in toy cases and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.
Diverse Imitation Learning via Self-Organizing Generative Models
Venue: arXiv cs.LG 2022
Year: 2022

Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exhibits a mixture of behaviors. Prior work has introduced latent variables to model variations of the expert policy. However, our experiments show that the existing works do not exhibit appropriate imitation of individual modes. To tackle this problem, we adopt an encoder-free generative model for behavior cloning (BC) to accurately distinguish and imitate different modes. Then, we integrate it with GAIL to make the learning robust towards compounding errors at unseen states. We show that our method significantly outperforms the state of the art across multiple experiments.
Fine-grained Intent Classification in the Legal Domain
Venue: arXiv cs.CL 2022
Year: 2022

A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corresponding to the case be clearly understood. In this paper, we introduce a dataset of 93 legal documents, belonging to the case categories of either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing intent same as the category of the document are annotated. Also, we annotate fine-grained intents for each such phrase to enable a deeper understanding of the case for a reader. Finally, we analyze the performance of several transformer-based models in automating the process of extracting intent phrases (both at a coarse and a fine-grained level), and classifying a document into one of the possible 4 categories, and observe that, our dataset is challenging, especially in the case of fine-grained intent classification.
Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm
Venue: arXiv cs.CV 2022
Year: 2022

Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-label. Finally we train the segmentation network based on pseudo-labels with weight transfer from self-supervision. Sufficient experimental results show that our proposed method combining self-supervision and weak supervision can almost approach the performance of purely fully supervised models while greatly reducing the required labeling time and dataset size.
Near-Field Rainbow: Wideband Beam Training for XL-MIMO
Venue: arXiv cs.IT 2022
Year: 2022

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve Tbps data rates in future 6G systems through beamforming and spatial multiplexing. Due to the extensive bandwidth and the huge number of antennas for wideband XL-MIMO, a significant near-field beam split effect will be induced, where beams at different frequencies are focused on different locations. The near-field beam split effect results in a severe array gain loss, so existing works mainly focus on compensating for this loss by utilizing the time delay (TD) beamformer. By contrast, this paper demonstrates that although the near-field beam split effect degrades the array gain, it also provides a new possibility to realize fast near-field beam training. Specifically, we first reveal the mechanism of the near-field controllable beam split effect. This effect indicates that, by dedicatedly designing the delay parameters, a TD beamformer is able to control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, this effect is also termed as the near-field rainbow in this paper. Then, taking advantage of the near-field rainbow effect, a fast wideband beam training scheme is proposed. In our scheme, the close form of the beamforming vector is elaborately derived to enable beams at different frequencies to be focused on different desired locations. By this means, the optimal beamforming vector with the largest array gain can be rapidly searched out by generating multiple beams focused on multiple locations simultaneously through only one radio-frequency (RF) chain. Finally, simulation results demonstrate the proposed scheme is able to realize near-optimal nearfield beam training with a very low training overhead.
Graph Spectral Embedding using the Geodesic Betweeness Centrality
Venue: arXiv cs.LG 2022
Year: 2022

We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation. Unlike embeddings based on the eigenvectors of the Laplacian, GSE incorporates two or more basis functions, for instance using the Laplacian and the affinity matrix. Such basis functions are constructed not from the original graph, but from one whose weights measure the centrality of an edge (the fraction of the number of shortest paths that pass through that edge) in the original graph. This allows more flexibility and control to represent complex network structure and shows significant improvements over the state of the art when used for data analysis tasks such as predicting failed edges in material science and network alignment in the human-SARS CoV-2 protein-protein interactome.
SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph
Venue: arXiv cs.CL 2022
Year: 2022

Entity alignment is the task of finding entities representing the same real-world object in two knowledge graphs(KGs). Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of great significance to the NLP applications and multi-language KGs fusion. In the task of aligning cross-language knowledge graphs, the structures of the two graphs are very similar, and the equivalent entities often have the same subgraph structure characteristics. The traditional GCN method neglects to obtain structural features through representative parts of the original graph and the use of adjacency matrix is not enough to effectively represent the structural features of the graph. In this paper, we introduce the subgraph network (SGN) method into the GCN-based cross-lingual KG entity alignment method. In the method, we extracted the first-order subgraphs of the KGs to expand the structural features of the original graph to enhance the representation ability of the entity embedding and improve the alignment accuracy. Experiments show that the proposed method outperforms the state-of-the-art GCN-based method.
Slicing of Probabilistic Programs based on Specifications
Venue: arXiv cs.PL 2022
Year: 2022

This paper presents the first slicing approach for probabilistic programs based on specifications. We show that when probabilistic programs are accompanied by their specifications in the form of pre- and post-condition, we can exploit this semantic information to produce specification-preserving slices strictly more precise than slices yielded by conventional techniques based on data/control dependency. To achieve this goal, our technique is based on the backward propagation of post-conditions via the greatest pre-expectation transformer -- the probabilistic counterpart of Dijkstra weakest pre-condition transformer. The technique is termination-sensitive, allowing to preserve the partial as well as the total correctness of probabilistic programs w.r.t. their specifications. It is modular, featuring a local reasoning principle, and is formally proved correct. As fundamental technical ingredients of our technique, we design and prove sound verification condition generators for establishing the partial and total correctness of probabilistic programs, which are of interest on their own and can be exploited elsewhere for other purposes. On the practical side, we demonstrate the applicability of our approach by means of a few illustrative examples and a case study from the probabilistic modelling field. We also describe an algorithm for computing least slices among the space of slices derived by our technique.
Max-min Rate Deployment Optimization for Backhaul-limited Robotic Aerial 6G Small Cells
Venue: arXiv cs.NI 2022
Year: 2022

To overcome the limited on-board battery issue of nominal airborne base stations (ABSs), we are exploring the use of robotic airborne base station (RABS) with energy neutral grasping end-effectors that are able to autonomously perch at tall urban landforms. Specifically, this paper studies a heterogeneous network (HetNet) assisted by a movable RABS as a small cell which connects to a macro base station (MBS) through a limited-capacity wireless backhaul link, which can be deemed as another major challenge. To exploit the potential gains that the mobility of RABS can bring in the system, the minimum rate among all users is maximized by jointly optimizing the RABS deployment, user association and subcarrier allocation. This problem is initially formulated as a binary polynomial optimization (BPO) problem. After reformulating it as a nonconvex quadratically constrained quadratic programming (QCQP), we propose a semidefinite relaxation (SDR) based heuristic method to capture a high-quality solution in polynomial time. Numerical results reveal that deploying a RABS as the small cell can improve the minimum data rate by 95.43% at most and 33.97% on average, and the developed SDR heuristic algorithm significantly outperforms the linear relaxation (LR) baseline method.
Resilient Edge: Can we achieve Network Resiliency at the IoT Edge using LPWAN and WiFi?
Venue: arXiv cs.NI 2022
Year: 2022

Edge computing has gained attention in recent years due to the adoption of many Internet of Things (IoT) applications in domestic, industrial and wild settings. The resiliency and reliability requirements of these applications vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. The network connectivity of IoT edge devices remains the central critical component that needs to meet the time-bounded Quality of Service (QoS) and fault-tolerance guarantees of the applications. Therefore, in this work, we systematically investigate how to meet IoT applications mixed-criticality QoS requirements in multi-communication networks. We (i) present the network resiliency requirements of IoT applications by defining a system model (ii) analyse and evaluate the bandwidth, latency, throughput, maximum packet size of many state-of-the-art LPWAN technologies, such as Sigfox, LoRa, and LTE (CAT-M1/NB-IoT) and Wi-Fi, (iii) implement and evaluate an adaptive system Resilient Edge and Criticality-Aware Best Fit (CABF) resource allocation algorithm to meet the application resiliency requirements using Raspberry Pi 4 and Pycom FiPy development board having five multi-communication networks. We present our findings on how to achieve 100% of the best-effort high criticality level message delivery using multi-communication networks
End-to-End Rubbing Restoration Using Generative Adversarial Networks
Venue: arXiv cs.CV 2022
Year: 2022

Rubbing restorations are significant for preserving world cultural history. In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. Specifically, we collect characters from the Zhang Menglong Bei and build up the first rubbing restoration dataset. We design the first generative adversarial network for rubbing restoration. Based on the dataset we collect, we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore the characters. The results of experiments show that RubbingGAN can repair both slightly and severely incomplete rubbing characters fast and effectively.
Reasoning about inter-procedural security requirements in IoT applications
Venue: arXiv cs.CR 2022
Year: 2022

The importance of information security dramatically increased and will further grow due to the shape and nature of the modern computing industry. Software is published at a continuously increasing pace. The Internet of Things and security protocols are two examples of domains that pose a great security challenge, due to how diverse the needs for those software may be, and a generalisation of the capabilities regarding the toolchain necessary for testing is becoming a necessity. Oftentimes, these software are designed starting from a formal model, which can be verified with appropriate model checkers. These models, though, do not represent the actual implementation, which can deviate from the model and hence certain security properties might not be inherited from the model, or additional issues could be introduced in the implementation. In this paper we describe a proposal for a novel technique to assess software security properties from LLVM bitcode. We perform various static analyses, such as points-to analysis, call graph and control-flow graph, with the aim of deriving from them an 'accurate enough' formal model of the paths taken by the program, which are then going to be examined via consolidated techniques by matching them against a set of defined rules. The proposed workflow then requires further analysis with more precise methods if a rule is violated, in order to assess the actual feasibility of such path(s). This step is required as the analyses performed to derive the model to analyse are over-approximating the behaviour of the software.
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Venue: arXiv cs.CL 2022
Year: 2022

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.